DEV Community: Veronica Nigro

Do you really need AI Agents?

Veronica Nigro — Fri, 10 Jan 2025 10:59:46 +0000

The rise of AI agents has introduced a groundbreaking way to handle complex workflows. These systems allow Large Language Models (LLMs) to determine workflows dynamically, offering flexibility where traditional, pre-determined processes fall short. However, agents aren’t always the right choice. Sometimes, simplicity and deterministic workflows deliver better results. So, how do you decide when to use agents and when to avoid them? Let’s explore.

The Case for Deterministic Workflows

In many cases, pre-determined workflows provide all the functionality needed to address a specific problem. These workflows are tightly defined and coded, offering simplicity, reliability, and robustness.

Take the example of a surfing trip website. Imagine your app handles two primary customer requests:

Searching for trip details — You could offer a search bar that connects users to your knowledge base.
Speaking to sales — A simple contact form could direct inquiries to your sales team.

If your users’ needs are confined to these two buckets, deterministic workflows are your best bet. Coding these flows ensures a 100% reliable system with no risk of error introduced by an LLM interpreting a user’s intent incorrectly.

By avoiding agentic behaviour in such scenarios, you benefit from:

Simplicity — No need to manage the complexity of multi-step AI decision-making.
Reliability — The system always follows a predictable path.
Cost-Efficiency — Pre-determined workflows are resource-light compared to the computational requirements of agents.

If your workflow can handle all reasonable queries without deviation, there’s no need to complicate things with agents.

When Deterministic Workflows Fall Short

Now, let’s look at situations where a rigid, pre-determined approach won’t cut it. Real-world problems are rarely so neatly defined. When workflows involve ambiguous, multi-faceted requests that can’t be easily categorized, you may need the flexibility of an agentic system.

Consider this example:

A user asks, “I can arrive Monday but might be delayed until Wednesday. Can I surf on Tuesday morning, and can I add cancellation insurance?”

This request involves multiple layers:

Checking availability on specific days.
Evaluating cancellation insurance options.
Understanding personal schedules and logistical details.

A deterministic workflow would struggle to handle such nuanced and variable queries. You’d have to pile on endless “if/else” conditions, making the system cumbersome and prone to errors.

Where Agents Shine

In situations where workflows can’t be neatly defined in advance, agents offer the flexibility needed to respond to complex requests. An agentic system could handle the surfing trip example above by dynamically accessing various tools and APIs:

Weather API — To provide accurate forecasts.
Google Maps API — For calculating travel distances.
Employee Availability Dashboard — To check staff schedules.
RAG System — To retrieve relevant answers from your knowledge base.

Agents allow systems to break free from rigid workflows, adapting to user requests on the fly. This makes them ideal for solving problems in unpredictable or high-variability domains.

The Evolution from Static to Dynamic Systems

Before agents, computer programs relied heavily on pre-determined workflows and logic gates. They were effective for narrow tasks — like summing numbers or finding the shortest path — but lacked the adaptability to handle complex, real-world scenarios.

Agents, powered by LLMs, are changing the game by opening up new possibilities. Instead of piling on conditionals, they can autonomously navigate complex environments and integrate multiple data sources to find optimal solutions.

For example, in the surfing trip case, the agent wouldn’t simply follow a static path. It would:

Analyze the user’s intent.
Gather relevant data from APIs.
Dynamically generate a solution.

This level of adaptability isn’t just convenient; it’s transformative. It allows software to tackle tasks previously reserved for human judgment.

When Not to Use Agents

While agents can handle complexity, they’re not always the best choice. Overusing them can introduce unnecessary risks and costs. You should avoid agents when:

The workflow is well-defined
If the task can be broken into predictable steps, a deterministic system is more efficient and reliable.
Error tolerance is low
Agents, powered by LLMs, are probabilistic systems, meaning they may occasionally make incorrect decisions. For applications where accuracy is critical, avoid relying on agents.
Cost and performance are priorities
Running an agent system requires more computational resources, which can increase costs and latency. In contrast, deterministic workflows are lightweight and fast.

Finding the Right Balance

Ultimately, the decision to use agents depends on your application’s needs. Here’s a quick guide:

Use Deterministic Workflows for tasks that are repetitive, predictable, and require high reliability. Examples include:

Basic customer support systems.
Simple e-commerce workflows.
Data entry applications.

Use Agents for tasks involving ambiguity, variability, or high complexity. Examples include:

Dynamic customer support with highly personalized queries.
Workflow orchestration in domains like logistics or healthcare.
Research tasks requiring integration of diverse data sources.

Agents are a powerful tool, but like any tool, they have their place. When workflows are straightforward, stick to deterministic systems for simplicity, reliability, and cost-efficiency. However, when you face unpredictable, multi-step tasks, agents shine by providing the flexibility and adaptability you need.

This is where platforms like mkinf come in. mkinf makes it easy to integrate agents into your workflows while giving you the tools to optimize performance, adapt to complex demands, and maintain production-ready reliability. Whether you need deterministic workflows or cutting-edge agentic solutions, mkinf helps you navigate the balance to build effective and efficient systems.

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Limitations and Challenges of AI Agents

Veronica Nigro — Fri, 10 Jan 2025 10:53:01 +0000

AI agents have taken center stage in automating tasks and enhancing productivity, making them a hot topic across industries. From answering nuanced questions to orchestrating complex workflows, their capabilities are impressive — but far from perfect. Building and deploying AI agents comes with significant challenges that developers and organizations must address to unlock their full potential.

Here’s a closer look at the key limitations of AI agents, from the intricacies of building them to ensuring they run reliably at scale.

Writing AI Agents from scratch

One of the toughest parts of building AI agents is that, for now, you’re largely starting from scratch. Sure, frameworks like LangChain or Haystack can help with some of the heavy lifting, but they don’t cover everything. You’re still left figuring out how to tie it all together, which can feel like solving a giant jigsaw puzzle with missing pieces.

Now, let’s say you find a cool tool on GitHub that could make your AI agent smarter or faster. Maybe it’s a script for scraping specific types of data or an algorithm that performs advanced analysis. It’s exciting — until you try to actually use it. More often than not, these tools aren’t plug-and-play. You might run into vague or incomplete documentation, leaving you scratching your head about how to even start. Worse, the tool might not be hosted anywhere, meaning it’s up to you to set up a server or cloud instance just to get it running.

Even when things seem straightforward, integrating a new tool into your AI agent can quickly turn into a rabbit hole. Does the tool’s output match what your agent expects? If not, you’ll need to write extra code to make it fit. What happens if the tool needs updates or suddenly stops working? Now you’re not just building an AI agent — you’re managing an ecosystem of moving parts, each of which requires constant attention.

Context handling

One of the most impressive feats of an AI agent is maintaining context over long interactions or multi-step workflows. But let’s be honest — this is easier said than done. Without proper context management, agents can “lose the plot” midway, forgetting previous inputs or misunderstanding follow-up instructions.

For example, imagine an AI agent helping plan a trip. If it forgets the location you mentioned in the first step, the entire interaction breaks down. Context handling challenges include:

Memory limits: Large language models (LLMs) have token limits, making it difficult to manage extended conversations or tasks.
State management: Ensuring the agent retains and applies relevant information across multiple steps.

Without strong context-handling mechanisms, agents struggle to deliver reliable and coherent outputs.

Prompt maintenance

AI agents rely on carefully crafted prompts to function effectively. Think of prompts as the “instructions” that guide the agent. However, maintaining prompts over time is a challenge, especially as the agent grows in complexity.

For example:

Adding a new tool or API may require revising existing prompts to ensure compatibility.
Poorly structured prompts can lead to ambiguous or incorrect responses.
Keeping track of prompt changes across different versions of the agent can become a logistical headache.

Without a robust system for managing and refining prompts, even well-designed agents can falter.

Error handling

No system is perfect, and AI agents are no exception. APIs go down, tools fail, and network issues crop up. What happens when something breaks? Without proper fallback mechanisms, many agents simply crash or return an error message.

Effective error handling requires:

Backup strategies: If one tool or API fails, the agent should try another approach or notify the user gracefully.
Resilience testing: Anticipating potential failures and building systems that can recover quickly.

Developers often underestimate the complexity of error handling, but it’s a crucial component of any robust AI agent.

Security and privacy

When agents interact with sensitive data — whether it’s personal information, financial records, or proprietary business data — security becomes a top priority. Every integration with an external tool or API introduces potential vulnerabilities. Key concerns include:

Data encryption: Ensuring all communication between the agent and tools is secure.
Access control: Limiting who or what can access sensitive data.
Compliance: Meeting regulations like GDPR, HIPAA, or other industry-specific standards.

Without a strong focus on security, agents risk becoming liabilities rather than assets.

Scalability

As AI agents grow in complexity, so do the challenges of scaling them effectively. Adding more tools, APIs, or even other agents to the system creates a web of dependencies that can quickly become unwieldy. Common scalability challenges include:

Performance bottlenecks: Ensuring the system remains fast and responsive as the workload increases.
Orchestration at scale: Managing workflows involving multiple agents and tools without delays or errors.
Cost management: Scaling often requires more computational resources, which can drive up costs.

Without careful planning and optimization, scaling an AI agent can lead to degraded performance and spiraling expenses.

How platforms like mkinf help

With all these challenges, it’s no wonder that developing and deploying AI agents often feels overwhelming. That’s where platforms like mkinf step in. By offering deployment-ready, customizable building blocks, mkinf allows developers to bypass many of these hurdles without sacrificing flexibility.

Instead of writing every component from scratch, you can leverage mkinf’s pre-built tools and APIs, which are not only designed to work seamlessly together but also customizable to fit your specific project needs. This approach takes the pain out of integration, simplifies prompt maintenance, and ensures robust error handling. It’s also built to scale, so your agents can grow alongside your ambitions, all while keeping security and privacy front and center.

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So, what actually defines an AI Agent?

Veronica Nigro — Sun, 05 Jan 2025 12:59:56 +0000

Is it really just an LLM with access to external APIs?

Pretty much. Let’s clear the air by saying that when we talk about AI agents, we’re primarily referring to LLM (large language model) agents. Imagine using a general-purpose LLM like ChatGPT, but instead of relying on it “as is,” you equip it with tools to expand its capabilities.

For instance, let’s say you ask ChatGPT about the weather tomorrow. By itself, an LLM can’t answer this because it doesn’t have real-time access to data. However, if we provide it with a tool — like an API that takes a date and location as input and returns weather information — it can retrieve and present that data. This is an example of an LLM leveraging an external tool.

What’s fascinating is that these tools can be virtually any piece of code, such as a weather API, a calculator, or even a complex database query. A practical example would be ChatGPT Search, which acts as an agent. When tasked with searching the web, it uses a dedicated tool to process a query and return internet results.

What’s even more interesting is that a tool can also be another AI agent. This creates the possibility of agents calling other agents in a recursive manner, each leveraging the others’ strengths. The real challenge, then, is designing architectures that distribute tasks effectively across these agents, ensuring they work together seamlessly in agentic projects.

Where to start when building an AI Agent?

From scratch. For now, at least. While much of coding has been consolidated into libraries and reusable functions, AI agent development is still in its early days.

Some startups are popping up with marketplaces of closed-source tools that can be integrated into your projects (examples could include AssemblyAI or LangChain’s toolsets), and you want more flexibility, there is always GitHub. Platforms like mkinf are stepping in to bridge this gap by hosting customizable tools you can integrate with just an API call.

What are the frameworks for?

Frameworks like LangChain and Haystack simplify the complex orchestration required to make AI agents function effectively. Think of them as the glue that holds everything together. When you’re building an AI agent, you’re essentially creating a system that needs to juggle multiple tasks: connecting to tools, managing workflows, keeping track of conversations, and sometimes even chaining multiple agents together. That’s where frameworks step in.

The Future of AI Agents

AI agents are undoubtedly a fascinating and rapidly evolving field. They promise a level of autonomy and adaptability that could redefine how we interact with technology. From automating complex workflows to enabling new levels of collaboration between systems, the possibilities seem endless.

Whether you’re a curious developer, a business looking to integrate AI agents, or just someone intrigued by the concept, there’s no better time to dive in. The field is still young, and the opportunity to shape its future is wide open.

Don’t miss out on what’s coming and follow mkinf on X or join our slack community